IDEAS home Printed from
   My bibliography  Save this article

Statistical Models for Analysis of Social Network Dynamics in Educational Studies




Sofia Dokuka - Candidate of Sciences in Sociology, Junior Researcher, Center for Institutional Studies, National Research University-Higher School of Economics. Email: sdokuka@hse.ruDiliara Valeeva - Junior Researcher, Center for Institutional Studies, National Research University-Higher School of Economics. E-mail: dvaleeva@hse.ruAddress: 24 Myasnitskaya str., 101000, Moscow, Russian Federation.Research on co-evolution of networks and behavior became possible with the emergence of student performance and behavior dynamic data collection and storage tools, as well as with the development of new social network analysis methods. Dynamic network analysis answers the question, how specific forms of student behavior, like bad habits, develop and propagate. It is also helpful in following how school or university students enter into friendship or antagonism with each other, as well as in assessing the impact social relations have on academic performance. The paper gives a review of the two key methods used for empirical analysis of social network dynamics. Stochastic Actor-Oriented Models (SAOM) represent one of the most elaborated techniques of social network dynamics investigation. This approach regards the present state of a network as dependable on its preceding state uniquely. Network evolution appears to be continuous, not discrete, so that a structural macro change is, in fact, a multitude of micro changes. Instead of dealing with structure of the network and prerequisites for its development, the SAOM model studies the processes underlying any changes recorded. Separable Temporal Exponential Random Graph Models (STERGM) are an alternative approach towards research on social network dynamics. In this case, the social network observed is a materialization of one of possible networks with predetermined characteristics. Any network develops in the result of a stochastic process, and the research should be aimed at discovering what forces this process is driven by. Comparison of an empirically discovered network with networks of similar size reveals structural features of the network and characteristics of actors who influenced the process of its establishment. In this paper, we also give an example of how both models can be used with the same set of data.DOI: 10.17323/1814-9545-2015-1-201-213

Suggested Citation

  • Sofia Dokuka & Diliara Valeeva, 2015. "Statistical Models for Analysis of Social Network Dynamics in Educational Studies," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 201-213.
  • Handle: RePEc:nos:voprob:2015:i:1:p:201-213

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    1. Gerhard G. Van De Bunt & Marijtje A.J. Van Duijn & Tom A.B. Snijders, 1999. "Friendship Networks Through Time: An Actor-Oriented Dynamic Statistical Network Model," Computational and Mathematical Organization Theory, Springer, vol. 5(2), pages 167-192, July.
    2. Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Duxbury, Scott W, 2018. "Diagnosing Multicollinearity in Exponential Random Graph Models," SocArXiv 2tgm7, Center for Open Science.
    2. Ana M. Guerra & Felipe Montes & Andrés F. Useche & Ana María Jaramillo & Silvia A. González & Jose D. Meisel & Catalina Obando & Valentina Cardozo & Ruth F. Hunter & Olga L. Sarmiento, 2020. "Effects of a Physical Activity Program Potentiated with ICTs on the Formation and Dissolution of Friendship Networks of Children in a Middle-Income Country," IJERPH, MDPI, vol. 17(16), pages 1-21, August.
    3. Joshua Lospinoso & Michael Schweinberger & Tom Snijders & Ruth Ripley, 2011. "Assessing and accounting for time heterogeneity in stochastic actor oriented models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(2), pages 147-176, July.
    4. Tyler Prochnow & Meg Patterson & M. Renée Umstattd Meyer & Joseph Lightner & Luis Gomez & Joseph Sharkey, 2022. "Conducting Physical Activity Research on Racially and Ethnically Diverse Adolescents Using Social Network Analysis: Case Studies for Practical Use," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    5. Gaonkar, Shweta & Mele, Angelo, 2023. "A model of inter-organizational network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 82-104.
    6. Duxbury, Scott W, 2017. "Diagnosing Multicollinearity in Exponential Random Graph Models," OSF Preprints hz93j, Center for Open Science.
    7. Prochnow, Tyler & Patterson, Megan S. & Hartnell, Logan & West, Geoffrey & Umstattd Meyer, M. Renée, 2021. "Implications of race and ethnicity for child physical activity and social connections at summer care programs," Children and Youth Services Review, Elsevier, vol. 127(C).
    8. Jiang, Binyan & Li, Jialiang & Yao, Qiwei, 2023. "Autoregressive networks," LSE Research Online Documents on Economics 119983, London School of Economics and Political Science, LSE Library.
    9. Ryohei Hisano & Tsutomu Watanabe & Takayuki Mizuno & Takaaki Ohnishi & Didier Sornette, 2016. "The gradual evolution of buyer-seller networks and their role in aggregate fluctuations," UTokyo Price Project Working Paper Series 068, University of Tokyo, Graduate School of Economics.
    10. Mark Huisman & Tom A. B. Snijders, 2003. "Statistical Analysis of Longitudinal Network Data With Changing Composition," Sociological Methods & Research, , vol. 32(2), pages 253-287, November.
    11. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    12. Maurits C. de Klepper & Giuseppe (Joe) Labianca & Ed Sleebos & Filip Agneessens, 2017. "Sociometric Status and Peer Control Attempts: A Multiple Status Hierarchies Approach," Journal of Management Studies, Wiley Blackwell, vol. 54(1), pages 1-31, January.
    13. Elina H. Hwang & Xitong Guo & Yong Tan & Yuanyuan Dang, 2022. "Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity," Information Systems Research, INFORMS, vol. 33(2), pages 515-539, June.
    14. Lee, Kevin H. & Xue, Lingzhou & Hunter, David R., 2020. "Model-based clustering of time-evolving networks through temporal exponential-family random graph models," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    15. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    16. Turnbull, Kathryn & Nemeth, Christopher & Nunes, Matthew & McCormick, Tyler, 2023. "Sequential estimation of temporally evolving latent space network models," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    17. Tom Broekel & Marcel Bednarz, 2018. "Disentangling link formation and dissolution in spatial networks: An Application of a Two-Mode STERGM to a Project-Based R&D Network in the German Biotechnology Industry," Networks and Spatial Economics, Springer, vol. 18(3), pages 677-704, September.
    18. Prasanta Bhattacharya & Tuan Q. Phan & Xue Bai & Edoardo M. Airoldi, 2019. "A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks," Service Science, INFORMS, vol. 30(1), pages 117-132, March.
    19. Karen Haandrikman & Leo J. G. Wissen, 2012. "Explaining the Flight of Cupid’s Arrow: A Spatial Random Utility Model of Partner Choice," European Journal of Population, Springer;European Association for Population Studies, vol. 28(4), pages 417-439, November.
    20. Siegwart Lindenberg, 2000. "It Takes Both Trust and Lack of Mistrust: The Workings of Cooperation and Relational Signaling in Contractual Relationships," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 4(1), pages 11-33, March.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nos:voprob:2015:i:1:p:201-213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marta Morozova (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.